Machine learning predicts nanoparticles’ structure and dynamics

Researchers at the University of Jyväskylä in Finland have demonstrated that new distance-based machine learning methods are capable of predicting structures and atomic dynamics of nanoparticles reliably. The new methods are significantly faster than traditional simulation methods used for nanoparticle research and will facilitate more efficient explorations of particle-particle reactions and particles’ functionality in their environment. The study was published in a Special Issue devoted to machine learning in The Journal of Physical Chemistry on May 15, 2020.